By David A. Winter(auth.)

Content material:
Chapter 1 Biomechanics as an Interdiscipline (pages 1–13):
Chapter 2 sign Processing (pages 14–44):
Chapter three Kinematics (pages 45–81):
Chapter four Anthropometry (pages 82–106):
Chapter five Kinetics: Forces and Moments of strength (pages 107–138):
Chapter 6 Mechanical paintings, power, and tool (pages 139–175):
Chapter 7 Three?Dimensional Kinematics and Kinetics (pages 176–199):
Chapter eight Synthesis of Human Movement—Forward options (pages 200–223):
Chapter nine Muscle Mechanics (pages 224–249):
Chapter 10 Kinesiological Electromyography (pages 250–280):
Chapter eleven Biomechanical move Synergies (pages 281–295):

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Additional resources for Biomechanics and Motor Control of Human Movement, Fourth Edition

Example text

1974). Carpenter, M. , J. S. Frank, D. A. Winter, and G. W. Paysar. “Sampling Duration Effects on Centre of Pressure Summary Measures,’’ Gait and Posture 13:35–40, 2001. Gage, W. , D. A. Winter, and J. S. Frank. “Kinematic and Kinetic Validation of Inverted Pendulum Model in Quiet Standing,’’ Gait and Posture 19: 124– 132, 2004. , D. E. Gregory, D. A. Winter, and J. P. Callaghan. “Gluteus Medius Muscle Activation Patterns as a Predictor of Low Back Pain during Standing,’’ Clinical Biomechanics 23:545–553, 2008.

Sampling Duration Effects on Centre of Pressure Summary Measures,’’ Gait and Posture 13:35–40, 2001. Gage, W. , D. A. Winter, and J. S. Frank. “Kinematic and Kinetic Validation of Inverted Pendulum Model in Quiet Standing,’’ Gait and Posture 19: 124– 132, 2004. , D. E. Gregory, D. A. Winter, and J. P. Callaghan. “Gluteus Medius Muscle Activation Patterns as a Predictor of Low Back Pain during Standing,’’ Clinical Biomechanics 23:545–553, 2008. , D. A. Winter, P. Stergiou, and S. E. Walt. “Anticipatory Control of Upper Body Balance during Human Locomotion,’’ Gait and Posture 2: 19–25, 1994.

19 (a) is a white noise signal simulated on Excel from a random number generator with amplitude ±1 sampled at a rate of 2048 samples/sec. Thus, the highest frequency present in this signal is the Nyquist frequency of 1024 Hz, so our FFT will cover frequencies from 0 Hz to 1024 Hz. 19 (b). 19 (a) A simulated white noise signal from a random number generator with amplitude ±1 samples at 2048 samples/sec. 19 (b) is the FFT of the white noise signal at 1 Hz intervals from 0 Hz to 1024 Hz. The “white” line passing through this FFT plot is a 40-point moving average showing that the signal has approximately equal power across the full spectrum of the signal.

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